Method and apparatus with battery management

ABSTRACT

A processor-implemented battery management method includes: estimating state information of a plurality of battery cells in a battery pack using a first battery state estimation model; determining whether state information of at least one of the plurality of battery cells is to be estimated using a second battery state estimation model; and estimating the state information of the at least one battery cell using the second model, in response to a result of the determining being that the state information of the at least one battery cell is to be estimated using the second model.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.16/512,484 filed on Jul. 16, 2019, which claims the benefit under 35 USC§ 119(a) of Korean Patent Application No. 10-2019-0003843 filed on Jan.11, 2019 in the Korean Intellectual Property Office, the entiredisclosures of which are incorporated herein by reference for allpurposes.

BACKGROUND 1. Field

The following description relates to a method and an apparatus withbattery management.

2. Description of Related Art

There are various methods of estimating a state of a battery. Forexample, a state of a battery may be estimated using a battery model,such as an equivalent circuit model or an electrochemical model.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

In one general aspect, a processor-implemented battery management methodincludes: estimating state information of a plurality of battery cellsin a battery pack using a first battery state estimation model;determining whether state information of at least one of the pluralityof battery cells is to be estimated using a second battery stateestimation model; and estimating the state information of the at leastone battery cell using the second model, in response to a result of thedetermining being that the state information of the at least one batterycell is to be estimated using the second model.

The first model may be a lightened model including any one or anycombination of any two or more of an equivalent circuit model, a currentintegration model, and a reduced order model to estimate stateinformation of a battery cell using less computation than the secondmodel.

The estimating of the state information of the plurality of batterycells in the battery pack using the first model may include determiningany one or any combination of any two or more of an internal resistanceof the equivalent circuit model, a capacitance of the equivalent circuitmodel, a representative potential of the reduced order model, and aconcentration of the reduced order model.

The second model may be a precise model including an electrochemicalmodel configured to estimate the state information of the at least onebattery cell by performing a greater amount of computations thanperformed by the first model in estimating the state information of theat least one battery cell.

The estimating the state information of the at least one battery cellusing the second model may include determining either one or both of aconcentration distribution and a potential in an electrode in theelectrochemical model.

The determining may include determining either one or both of whetherthe state information of the at least one battery cell estimated by thefirst model is abnormal state information and whether a preset time haselapsed since a previous estimation of battery cell state informationusing the second model.

The determining of whether the state information of the at least onebattery cell estimated by the first model is abnormal state informationmay include comparing a reference value to either one or both of thestate information of the at least one battery cell estimated by thefirst model, and a value derived from the state information of the atleast one battery cell estimated by the first model.

The previous estimation of battery cell state information using thesecond model may include a previous estimation of state information ofany one of the plurality of the battery cells using the second model.

The method of claim 1, wherein the determining comprises determiningthat the state information of the at least one battery cell is to beestimated using the second model in response to determining that thestate information of the at least one battery cell estimated by thefirst model is abnormal state information.

The determining may include: determining that state information of theat least one battery cell is to be estimated using the second modelbased on a predetermined battery cell selection scheme, in response to apreset time elapsing.

The predetermined battery cell selection scheme may include either oneor both of: a round-robin scheme wherein the at least one battery cellis selected from among the plurality of battery cells based on asequential order of the plurality of battery cells; and a random schemewherein the at least one battery cell is randomly selected from amongthe plurality of battery cells.

The determining may include determining whether a difference between thestate information of the at least one battery cell estimated by thefirst model and the state information of another battery cell of theplurality of battery cells estimated by the first model is greater thanor equal to a reference value.

The determining may include: determining a rate of change over time ofthe state information of the at least one battery cell estimated by thefirst model; and determining whether the rate of change is greater thanor equal to a reference rate to indicate whether the state informationof the at least one of the plurality of battery cells is to be estimatedusing the second model.

The determining may include determining whether the state information ofthe at least one battery cell estimated by the first model is within areference range to indicate whether the state information of the atleast one of the plurality of battery cells is to be estimated using thesecond model.

The method of claim 1, wherein the determining comprises determiningwhether a preset time has elapsed since a previous estimation of batterycell state information using the second model to indicate whether thestate information of the at least one of the plurality of battery cellsis to be estimated using the second model.

The determining may include: determining whether the state informationof any one of the plurality of battery cells estimated by the firstmodel is abnormal state information; determining whether a preset timehas elapsed since a previous estimation of battery cell stateinformation using the second model, in response to determining that noneof the state informations of the plurality of battery cells are abnormalstate information; and determining that the state information of the atleast one battery cell is to be estimated using the second model inresponse to either one of determining that the state information of theat least one battery cell estimated by the first model is abnormal stateinformation, and determining that the preset time has elapsed.

The determining may include determining that the state information ofthe at least one battery cell is not to be estimated using the secondmodel in response to determining that none of the state informations ofthe plurality of battery cells are abnormal state information and thepreset time has not elapsed.

The method may include: verifying whether the state information of theat least one battery cell estimated using the second model is abnormalstate information; and transmitting, to an external system, informationindicating that the state information of the at least one battery cellis abnormal, in response to verifying that the state information of theat least one battery cell estimated using the second model is abnormalstate information.

The state information of the plurality of battery cells may includeeither one or both of states of charge (SOCs) and states of health(SOHs) of the plurality of battery cells.

A non-transitory computer-readable storage medium may store instructionsthat, when executed by one or more processors, cause the one or moreprocessors to perform method.

In another general aspect, a battery management apparatus includes: oneor more processors configured to: estimate state information of aplurality of battery cells in a battery pack using a first battery stateestimation model, determine whether state information of at least one ofthe plurality of battery cells is to be estimated using a second batterystate estimation model, and estimate the state information of the atleast one battery cell using the second model, in response todetermining that the state information of the at least one battery cellis to be estimated using the second model.

For the determining, the one or more processors may be configured todetermine either one or both of whether the state information of the atleast one battery cell estimated by the first model is abnormal stateinformation and whether a preset time has elapsed since a previousestimation of battery cell state information using the second model.

For the determining, the one or more processors may be configured todetermine that the state information of the at least one battery cell isto be estimated using the second model in response to determining thatthe state information of the at least one battery cell estimated by thefirst model is abnormal state information.

For the determining, the one or more processors may be configured todetermine that state information of the at least one battery cell is tobe estimated using the second model based on a predetermined batterycell selection scheme, in response to a preset time elapsing.

The apparatus may include a memory storing instructions that, whenexecuted by the one or more processors, configure the one or moreprocessors to perform: the estimating of the state information of theplurality of battery cells using the first model, the determining ofwhether the state information of the at least one of the plurality ofbattery cells is to be estimated using the second model, and theestimating of the state information of the at least one battery cellusing the second model.

Other features and aspects will be apparent from the following detaileddescription, the drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example of a battery system.

FIG. 2 illustrates an example of using a lightened model and a precisemodel.

FIG. 3 illustrates an example of a battery management method.

FIG. 4 illustrates an example of determining whether to perform preciseestimation.

FIG. 5 illustrates an example of determining whether there is a batterycell having abnormal state information.

FIG. 6 illustrates an example of a battery system.

FIG. 7 illustrates an example of a battery management apparatus.

FIGS. 8 and 9 illustrate examples of a vehicle.

Throughout the drawings and the detailed description, unless otherwisedescribed or provided, the same drawing reference numerals will beunderstood to refer to the same elements, features, and structures. Thedrawings may not be to scale, and the relative size, proportions, anddepiction of elements in the drawings may be exaggerated for clarity,illustration, and convenience.

DETAILED DESCRIPTION

The following detailed description is provided to assist the reader ingaining a comprehensive understanding of the methods, apparatuses,and/or systems described herein. However, various changes,modifications, and equivalents of the methods, apparatuses, and/orsystems described herein will be apparent after an understanding of thedisclosure of this application. For example, the sequences of operationsdescribed herein are merely examples, and are not limited to those setforth herein, but may be changed as will be apparent after anunderstanding of the disclosure of this application, with the exceptionof operations necessarily occurring in a certain order. Also,descriptions of features that are known in the art may be omitted forincreased clarity and conciseness.

The features described herein may be embodied in different forms, andare not to be construed as being limited to the examples describedherein. Rather, the examples described herein have been provided merelyto illustrate some of the many possible ways of implementing themethods, apparatuses, and/or systems described herein that will beapparent after an understanding of the disclosure of this application.

Although terms such as “first,” “second,” and “third” may be used hereinto describe various members, components, regions, layers, or sections,these members, components, regions, layers, or sections are not to belimited by these terms. Rather, these terms are only used to distinguishone member, component, region, layer, or section from another member,component, region, layer, or section. Thus, a first member, component,region, layer, or section referred to in examples described herein mayalso be referred to as a second member, component, region, layer, orsection without departing from the teachings of the examples.

Throughout the specification, when a component is described as being“connected to,” or “coupled to” another component, it may be directly“connected to,” or “coupled to” the other component, or there may be oneor more other components intervening therebetween. In contrast, when anelement is described as being “directly connected to,” or “directlycoupled to” another element, there can be no other elements interveningtherebetween. Likewise, similar expressions, for example, “between” and“immediately between,” and “adjacent to” and “immediately adjacent to,”are also to be construed in the same way.

The terminology used herein is for describing various examples only andis not to be used to limit the disclosure. The articles “a,” “an,” and“the” are intended to include the plural forms as well, unless thecontext clearly indicates otherwise. The terms “comprises,” “includes,”and “has” specify the presence of stated features, numbers, operations,members, elements, and/or combinations thereof, but do not preclude thepresence or addition of one or more other features, numbers, operations,members, elements, and/or combinations thereof.

Unless otherwise defined, all terms, including technical and scientificterms, used herein have the same meaning as commonly understood by oneof ordinary skill in the art to which this disclosure pertains and basedon an understanding of the disclosure of the present application. Terms,such as those defined in commonly used dictionaries, are to beinterpreted as having a meaning that is consistent with their meaning inthe context of the relevant art and the disclosure of the presentapplication and are not to be interpreted in an idealized or overlyformal sense unless expressly so defined herein. The use of the term“may” herein with respect to an example or embodiment (e.g., as to whatan example or embodiment may include or implement) means that at leastone example or embodiment exists where such a feature is included orimplemented, while all examples are not limited thereto.

Hereinafter, examples are described in detail with reference to theaccompanying drawings. The following specific structural or functionaldescriptions are exemplary to merely describe the examples, and thescope of the examples is not limited to the descriptions provided in thepresent specification. Various changes and modifications can be madethereto by those of ordinary skill in the art. Like reference numeralsin the drawings denote like elements, and a known function orconfiguration will be omitted herein.

FIG. 1 illustrates an example of a battery system.

Referring to FIG. 1, a battery system 100 may include a batterymanagement apparatus 110 and a battery pack 120.

The battery pack 120 may include a plurality of battery modules, andeach of the battery modules may include a plurality of battery cells.The battery pack 120 may include a condenser or a secondary cellconfigured to store power as a result of a charging operation. Anapparatus including the battery pack 120 may supply power produced bythe battery pack 120 to a load.

The battery management apparatus 110 may be implemented by a batterymanagement system (BMS), and may perform, for example, an operation ofmonitoring a state of the battery pack 120, an operation of maintainingan optimized condition of the battery pack 120, an operation ofpredicting a time to replace the battery pack 120, an operation ofdetecting a problem of the battery pack 120, and/or an operation ofcontrolling a state or an operation of the battery pack 120 bygenerating a control or instruction signal related to the battery pack120.

The battery management apparatus 110 may obtain sensed information ofthe plurality of battery cells in the battery pack 120. For example, thesensed information may include voltage information, current information,and/or temperature information.

The battery management apparatus 110 may estimate battery stateinformation based on the sensed information, and output a correspondingresult. The battery state information may be state information of theplurality of battery cells. The state information may include, forexample, a state of charge (SOC) and/or a state of health (SOH).However, examples are not limited thereto.

The SOC may be a parameter indicating a charge level of a battery. TheSOC may indicate an amount of energy stored in the battery, and theamount may be indicated as 0% to 100%. For example, 0% may indicate afully discharged state and 100% may indicate a fully charged state. Sucha metric may be variously modified in varied examples, for example,defined depending on a design intention or an aspect of such examples.The SOC may be estimated or measured using various schemes.

The SOH may be a parameter quantitatively indicating a change in alifetime characteristic of the battery caused by deterioration. The SOHmay indicate, for example, a degradation level of a life or a capacityof the battery. The SOH may be estimated or measured using variousschemes. For example, the SOH may indicate an amount of batterydegradation of the battery, and the amount may be indicated as 0% to100%. For example, 0% may indicate a fully degraded battery and 100% mayindicate that the battery's conditions match the battery'sspecifications.

Hereinafter, an example of estimating battery state information will bedescribed in detail with reference to FIG. 2.

FIG. 2 illustrates an example of using a lightened model and a precisemodel.

Referring to FIG. 2, the battery management apparatus 110 may estimatestate information of the plurality of battery cells in the battery pack120 using at least one of a lightened model and a precise model.

The lightened model may be a model configured to estimate stateinformation using fewer computations than the computations used by theprecise model to estimate the state information, and the lightened modelmay include, for example, an equivalent circuit model, a currentintegration model, and/or a reduced order model (that is, a reducedorder electrochemical model). Since the lightened model may estimatestate information using fewer computations than the precise model, thelightened model may use a smaller amount of information than the precisemodel for estimating the state information, and an accuracy of the stateinformation estimated by the lightened model may be relatively low orlower compared to an accuracy of the state information estimated by theprecise model. The state information estimated by the lightened modelmay include (or may be based on) any one or any combination of any twoor more of an internal resistance of the equivalent circuit model, acapacitance of the equivalent circuit model, a representative potentialof the reduced order model, and a concentration of the reduced ordermodel.

Here, the equivalent circuit model may be a model configured to estimatean amount of power remaining in a battery by forming a virtual circuitusing a resistor and a capacitor to represent a voltage value changingwhen the battery is charged or discharged. The current integration modelmay be a model configured to estimate an amount of power remaining in abattery by summing up quantities of electric charges used for chargingor discharging the battery through a current sensor provided at aterminal of the battery. The reduced order model may be a modelconfigured to reduce a dimension of an internal physical phenomenon in abattery (for example, an ion concentration of the battery) to aone-dimension.

The precise model may be a model configured to estimate stateinformation using more computations than the computations used by thelightened model, and may include, for example, an electrochemical model.Since the precise model may estimate state information using morecomputations than the lightened model, the precise model may use agreater amount of information than the lightened model for estimatingthe state information, and an accuracy of the state informationestimated by the precise model may be relatively high or higher comparedto an accuracy of the state information estimated by the lightenedmodel. The state information estimated by the precise model may includeat least one of a concentration distribution and a potential in anelectrode in the electrochemical model. Here, the electrochemical modelmay be a model configured to estimate an amount of power remaining inthe battery by modeling an internal physical phenomenon in the battery,for example, an ion concentration of the battery, and may include, forexample, a full order model.

The battery management apparatus 110 may use the lightened model toestimate the state information of the plurality of battery cells in thebattery pack 120. The lightened model may perform a relatively smallamount of computations to estimate the state information (compared to,e.g., the precise model) and thus, the lightened model may be driven (orimplemented) in real time in the battery management apparatus 110. Thelightened model may estimate the state information of the plurality ofbattery cells based on a predetermined condition (for example,estimating the state information of the plurality of battery cells at aminimum update interval of 100 ms, as a non-limiting example).

The battery management apparatus 110 may temporarily use the precisemodel that uses a relatively large or larger amount of computations toestimate state information (compared to, e.g., the lightened model) whenit is advantageous to estimate state information of at least one of theplurality of battery cells in the battery pack 120 (e.g., a specificbattery cell or a specific group of battery cells in the battery pack120). Since a large amount of computations may be used to estimate thestate information using the precise model, it may be advantageous toavoid driving (or implementing) the precise model in real time in thebattery management apparatus 110, to thereby reduce the requiredprocessing power. Thus, instead of implementing the precise model inreal time, the precise model may be used to estimate state informationof some battery cells (e.g., specific battery cells) when preciseestimation of the state information of such battery cells isadvantageous.

In response to a determination that some battery cells include abnormalstate information as a result of estimating the state information of theplurality of battery cells based on the lightened model, the batterymanagement apparatus 110 may precisely estimate the state information ofthe corresponding battery cells based on the precise model. Throughthis, the battery management apparatus 110 may accurately determinewhether the corresponding abnormal state information is an estimationerror of the lightened model, or whether the corresponding battery cellsactually include abnormal state information, and the battery managementapparatus 110 may perform an appropriate succeeding operation.

Although abnormal state information may not be detected through thelightened model, the battery management apparatus 110 may select aportion of the plurality of battery cells, perform state informationestimation based on the precise model for the selected portion, and maycorrect or supplement an estimation result of the lightened model usinga result of the performing of the state information estimation based onthe precise model.

As described above, the battery management apparatus 110 may estimatestate information by switching between the lightened model and theprecise model, thereby monitoring state information in real time basedon the lightened model while improving an estimation accuracy byutilizing the precise model, which produces a greater processing loadthan the lightened model, even if hardware of the battery managementapparatus 110 includes low-specification hardware.

FIG. 3 illustrates an example of a battery management method.

Referring to FIG. 3, a battery management method performed by aprocessor (e.g., the processor may be representative of one or moreprocessors) included in a battery management apparatus is illustrated.

In operation 310, the battery management apparatus may estimate stateinformation of a plurality of battery cells in a battery pack based on alightened model. For example, the battery management apparatus mayestimate the state information of all the battery cells in the batterypack in real time based on the lightened model.

In operation 320, the battery management apparatus may determine whetherstate information of at least one of the plurality of battery cells isto be precisely estimated. For example, the battery management apparatusmay determine whether the state information of the at least one batterycell is to be precisely estimated, based on either one or both ofwhether there is at least one battery cell having abnormal stateinformation among the plurality of battery cells and whether a timepreset for precise estimation elapses. For example, in operation 320,the battery management apparatus may determine that state information ofat least one of the plurality of battery cells is to be preciselyestimated in response to determining that there is at least one batterycell having abnormal state information among the plurality of batterycells and/or determining that a time preset for precise estimation haselapsed. As another example, in operation 320, the battery managementapparatus may determine that state information of at least one of theplurality of battery cells is not to be precisely estimated in responseto determining that there is not at least one battery cell havingabnormal state information among the plurality of battery cells and/ordetermining that a time preset for precise estimation has not elapsed. Aprocess of determining whether state information of at least one batterycell is to be precisely estimated will be described further withreference to FIGS. 4 and 5.

In response to a determination that the state information of the atleast one battery cell is to be precisely estimated, the batterymanagement apparatus may estimate the state information of the at leastone battery cell based on a precise model, in operation 330. The batterymanagement apparatus may determine, through precise estimation, whetherthe abnormal state information of the battery cell estimated inoperation 310 is an error of the lightened model or whether thecorresponding battery cell actually includes abnormal state information(e.g., thereby determining that the abnormal state information of thebattery cell estimated in operation 310 is accurate).

In response to the state information estimated based on the precisemodel (e.g., the state information estimated in operation 330) beingdifferent from the state information estimated based on the lightenedmodel (e.g., the state information estimated in operation 310), thebattery management apparatus may manage the corresponding battery cellbased on the state information estimated based on the precise model (andnot based on the state information estimated based on the lightenedmodel, for example). As another example, in response to the stateinformation estimated based on the precise model being different fromthe state information estimated based on the lightened model, thebattery management apparatus may correct or supplement the stateinformation estimated based on the lightened model, based on the stateinformation estimated based on the precise model.

In response to a determination that the state information of the atleast one battery cell is not to be precisely estimated in operation320, operation 330 may not be performed. Instead, the battery managementapparatus may manage the plurality of battery cells based on the stateinformation estimated in operation 310.

FIG. 4 illustrates an example of determining whether to perform preciseestimation.

Referring to FIG. 4, a process of determining whether to perform preciseestimation (e.g., operation 320 of FIG. 3) performed by a processor(e.g., the processor may be representative of one or more processors)included in a battery management apparatus is illustrated.

In operation 410, the battery management apparatus may determine whetherthere is at least one battery cell having abnormal state informationamong a plurality of battery cells. For example, in operation 410, thebattery management apparatus may determine whether there is at least onebattery cell having abnormal state information, based on any one or anycombination of (a) whether there is at least one battery cell of which astate information difference from another battery cell is greater thanor equal to a reference value, among the plurality of battery cells, (b)whether there is at least one battery cell of which a rate of change instate information over time is greater than or equal to a referencerate, among the plurality of battery cells, and (c) whether there is atleast one battery cell of which state information is out of a referencerange, among the plurality of battery cells. For example, in operation410, the battery management apparatus may determine there is at leastone battery cell having abnormal state information in response todetermining that (a) there is at least one battery cell of which a stateinformation difference from another battery cell is greater than orequal to a reference value, among the plurality of battery cells, (b)there is at least one battery cell of which a rate of change in stateinformation over time is greater than or equal to a reference rate,among the plurality of battery cells, and/or (c) there is at least onebattery cell of which state information is out of a reference range,among the plurality of battery cells. In another example, in operation410, the battery management apparatus may determine there is not atleast one battery cell having abnormal state information in response todetermining that (a) there is not at least one battery cell of which astate information difference from another battery cell is greater thanor equal to a reference value, among the plurality of battery cells, (b)there is not at least one battery cell of which a rate of change instate information over time is greater than or equal to a referencerate, among the plurality of battery cells, and/or (c) there is not atleast one battery cell of which state information is out of a referencerange, among the plurality of battery cells.

In response to a determination that there is at least one battery cellhaving abnormal state information among the plurality of battery cells,the battery management apparatus may determine that the stateinformation of the at least one battery cell is to be preciselyestimated, and then operation 330 may be performed.

In response to a determination that there is not at least one batterycell having abnormal state information among the plurality of batterycells, the battery management apparatus may determine whether a timepreset for precise estimation elapses, in operation 420. For example,the battery management apparatus may determine whether the preset timehas elapsed since a point in time of a previous precise estimation.

Further, in response to an elapse of the time preset for preciseestimation, the battery management apparatus may determine that stateinformation of at least one battery cell selected based on apredetermined rule (e.g., a predetermined battery cell selectionschemed) from among the plurality of battery cells is to be preciselyestimated. For example, the predetermined rule may include a round-robinscheme which sequentially selects a battery cell, and/or a random schemewhich randomly selects a battery cell of which state information is tobe precisely estimated from among the plurality of battery cells.

As described above, although there may not be a battery cell havingabnormal state information, the battery management apparatus may selecta portion of the plurality of battery cells at a predetermined intervaland may perform precision estimation thereon, thereby maintaining arelatively high estimation accuracy.

In response to a determination that the time preset for preciseestimation is yet to elapse in operation 420, the battery managementapparatus may determine not to perform precise estimation.

FIG. 5 illustrates an example of determining whether there is a batterycell having abnormal state information.

Referring to FIG. 5, an example of detecting a battery cell havingabnormal state information is illustrated. In FIG. 5, a horizontal axisdenotes a point in time (e.g., wherein units of the horizontal axis areseconds, minutes, or any other unit of time), a vertical axis denotesidentification information of a plurality of battery cells (e.g., eachletter (a) through (f) may correspond a different battery cell), and anumber in a box denotes information estimated based on a lightenedmodel, which may be an example of state information of a predeterminedbattery cell at a predetermined point in time. The state information mayinclude, for example, a state of charge (SOC) and/or a state of health(SOH). For example, state information of a battery cell (a) at a pointin time 1 (e.g., at 1 second) may be 78 (e.g., 78% SOC or 78% SOH). FIG.5 illustrates an example of state information for ease of description.An operation of a battery management apparatus described herein is notlimited thereto.

For example, at the point in time 1, the battery management apparatusmay estimate state information of a plurality of battery cells based onthe lightened model. The battery management apparatus may notice ordetermine that state information 66 of a battery cell (c) issubstantially lower than state information of the other battery cells,and may detect the battery cell (c) as a battery cell having abnormalstate information. As described above, the battery management apparatusmay detect a battery cell of which state information is substantiallylower or higher than state information of the other battery cells, thatis, a battery cell of which a state information difference from anotherbattery cell is greater than or equal to a reference value, among theplurality of battery cells, as a battery cell having abnormal stateinformation.

Further, state information may decrease over time as power stored in abattery cell is used. The battery management apparatus may verify a rateof change in the state information by comparing state information of aplurality of points in time. The battery management apparatus maycompare state information at the point in time 1 and state informationat a point in time 2, thereby verifying a rate of change in the stateinformation during a corresponding period. In this example, the batterymanagement apparatus may notice that a rate of change in stateinformation of a battery cell (f) exceeds a non-limiting example 10%reference rate (e.g., the state information of battery (f) decreasesfrom 83 to 72 from point in time 1 to point in time 2, as shown in FIG.5), and may thereby detect the battery cell (f) as a battery cell havingabnormal state information. As described above, the battery managementapparatus may detect a battery cell of which a rate of change in stateinformation over time is greater than or equal to a reference rate as abattery cell having abnormal state information.

Further, at a point in time 50, the battery management apparatus mayestimate state information of the plurality of battery cells based onthe lightened model. The battery management apparatus may notice thatstate information 8 of a battery cell (b) is out of a safe range (forexample, a safe range of 10 to 95, as a non-limiting example) and maythereby detect the battery cell (b) as a battery cell having abnormalstate information. As described above, the battery management apparatusmay detect a battery cell of which state information is out of areference range as a battery cell having abnormal state information.

FIG. 6 illustrates an example of a battery system.

Referring to FIG. 6, the battery system 100 may include the batterymanagement apparatus 110, the battery pack 120, and sensors 611, 613,and 615. In the example of FIG. 6, the battery management apparatus 110may include an input buffer 620, a lightened model 630, a precise model640, a model changer 650, a scheduler 660, a memory 670, and an outputbuffer 680.

The voltage sensor 611 may sense voltages of the battery cells in thebattery pack 120 and may store voltage information in the input buffer620. The current sensor 613 may sense currents of the battery cells inthe battery pack 120 and may store current information in the inputbuffer 620. The temperature sensor 615 may sense temperatures of thebattery cells in the battery pack 120 and may store temperatureinformation in the input buffer 620.

The input buffer 620 may store the sensed information received from thesensors 611, 613, and 615. In this example, a clock may record a time atwhich the sensed information is stored.

The scheduler 660 may select a model to be used to estimate the stateinformation between the lightened model 630 and the precise model 640.In this example, the scheduler 660 may select one or both of thelightened model 630 and the precise model 640. For example, when thestate information of the plurality of battery cells is yet to beestimated, the scheduler 660 primarily may select the lightened model630.

The lightened model 630 may estimate the state information of theplurality of battery cells in the battery pack 120 based on the sensedinformation stored in the input buffer 620. The model changer 650 maydetermine whether a model change is needed based on the stateinformation estimated by the lightened model 630. For example, inresponse to a determination that the state information estimated by thelightened model 630 is abnormal, the model changer 650 may determine tochange a model to the precise model 640. Although it may not bedetermined that the state information estimated by the lightened model630 is abnormal, in response to an elapse of a time preset for preciseestimation, the model changer 650 may determine to change a model to theprecise model 640. In response to the model changer 650 determining tochange a model, the scheduler 660 may select a model corresponding tothe determination of the model changer 650. The model changer 650 maydirectly determine whether to use the precise model 640 based on thesensed information received from the input buffer 620. The model changer650 also may determine whether there is at least one battery cell havingabnormal state information based on whether there is at least onebattery cell of which a voltage is out of a predetermined voltage rangeamong the plurality of battery cells and/or whether there is at leastone battery cell of which a temperature is out of a predeterminedtemperature range among the plurality of battery cells.

In response to the precise model 640 being selected, the precise model640 may estimate state information of at least one of the plurality ofbattery cells based on the sensed information stored in the input buffer620. The state information estimated by the precise model 640 may bestored in the output buffer 680. With respect to a battery cell of whichstate information is not estimated by the precise model 640, stateinformation thereof estimated by the lightened model 630 may be storedin the output buffer 680.

The memory 670 may store model parameters of the lightened model 630 andthe precise model 640. The model parameters may be parameters learnedbefore state information estimation is performed.

In response to a determination that the state information estimated bythe precise model 640 as well as the state information estimated by thelightened model 630 is abnormal, the battery management apparatus 110may transmit a corresponding determination result to an external system.The battery management apparatus 110 may determine whether the abnormalstate information results from a simple unbalance between the pluralityof battery cells or results from abnormal deterioration, based on anestimation result of at least one of the lightened model 630 and theprecise model 640, and may transmit a corresponding determination resultto the external system. For example, the external system may be avehicle control unit (VCU), an electronic control unit (ECU), a chargerat an electric vehicle charging station, a measuring instrument at avehicle repair shop, a mobile terminal of a user using a deviceincluding the battery management apparatus 110, or a smart device.

In an example, the description provided with reference to FIGS. 1through 5 may apply to the battery management apparatus 110 of FIG. 6,and thus duplicated description will be omitted for conciseness.

FIG. 7 illustrates an example of a battery management apparatus.

Referring to FIG. 7, a battery management apparatus 700 may include amemory 710 and a processor 720 (e.g., the processor 720 may berepresentative of one or more processors). The memory 710 and theprocessor 720 may communicate with each other through a bus 730.

The memory 710 may include computer-readable instructions. The processor720 may perform the operations described above when the instructionsstored in the memory 710 are executed by the processor 720.

The processor 720 may estimate state information of a plurality ofbattery cells in a battery pack based on a lightened model, maydetermine whether state information of at least one of the plurality ofbattery cells is to be precisely estimated, and may estimate the stateinformation of the at least one battery cell based on a precise model,in response to a determination that the state information of the atleast one battery cell is to be precisely estimated.

The battery management apparatus 700 may estimate the state informationbased on the lightened model at all times, and may estimate a stateinformation based on the precise model irregularly or regularly, therebyestimating the state information in real time such that a minimum updateinterval is satisfied while providing a relatively high estimationaccuracy.

In addition, the battery management apparatus 700 may perform theoperations described above.

FIGS. 8 and 9 illustrate examples of a vehicle.

Referring to FIG. 8, an example embodiment vehicle 800 may include thebattery pack 120 and the battery management apparatus 110. In anotherexample, the vehicle 800 may be representative of the battery managementapparatus 110. The vehicle 800 may use the battery pack 120 as a powersource. The vehicle 800 may be, for example, an electric vehicle or ahybrid vehicle.

The battery pack 120 may include a plurality of battery modules. Abattery module may include a plurality of battery cells.

The battery management apparatus 110 monitors whether an abnormalityoccurs in the battery pack 120, and prevents over-charging orover-discharging of the battery pack 120. Further, the batterymanagement apparatus 110 may perform thermal control with respect to thebattery pack 120 in response to a temperature of the battery pack 120exceeding a first temperature, for example, 40° C., or being less than asecond temperature, for example, −10° C. In addition, the batterymanagement apparatus 110 may perform cell balancing such that chargestates of battery cells in the battery pack 120 are equalized.

When the battery pack 120 is partially or fully charged, the batterymanagement apparatus 110 may determine state information of each of thebattery cells in the battery pack 120 or state information of thebattery pack 120. Further, when the battery pack 120 is partially orfully discharged, the battery management apparatus 110 may determinestate information of each of the battery cells in the battery pack 120or state information of the battery pack 120.

The battery management apparatus 110 may transmit the state informationof the battery pack 120 to an ECU or a VCU of the vehicle 800. The ECUor the VCU of the vehicle 800 may output the state information of thebattery pack 120 on a display of the vehicle 800. As shown in theexample of FIG. 9, the ECU or the VCU may display the state informationof the battery pack 120 on a dashboard 910 of the vehicle 800. The ECUor the VCU may display a remaining travel distance determined based onthe estimated state information on the dashboard 910. The ECU or the VCUmay display the state information of the battery pack 120 and theremaining travel distance on a head-up display (HUD) of the vehicle 800.

In an example, the description provided with reference to FIGS. 1through 7 applies to the description of FIGS. 8 and 9, and thusduplicated description will be omitted for conciseness.

The battery management apparatuses, battery management apparatus 110,battery pack 120, sensor 611, sensor 613, sensor 615, input buffer 620,lightened model 630, precise model 640, model changer 650, scheduler660, memory 670, output buffer 680, battery management apparatus 700,memory 710, processor 720, bus 730, vehicle 800, dashboard 910, andother apparatuses, modules, devices, and other components describedherein with respect to FIGS. 1-9 are implemented by or representative ofhardware components. Examples of hardware components that may be used toperform the operations described in this application where appropriateinclude controllers, sensors, generators, drivers, memories,comparators, arithmetic logic units, adders, subtractors, multipliers,dividers, integrators, and any other electronic components configured toperform the operations described in this application. In other examples,one or more of the hardware components that perform the operationsdescribed in this application are implemented by computing hardware, forexample, by one or more processors or computers. A processor or computermay be implemented by one or more processing elements, such as an arrayof logic gates, a controller and an arithmetic logic unit, a digitalsignal processor, a microcomputer, a programmable logic controller, afield-programmable gate array, a programmable logic array, amicroprocessor, or any other device or combination of devices that isconfigured to respond to and execute instructions in a defined manner toachieve a desired result. In one example, a processor or computerincludes, or is connected to, one or more memories storing instructionsor software that are executed by the processor or computer. Hardwarecomponents implemented by a processor or computer may executeinstructions or software, such as an operating system (OS) and one ormore software applications that run on the OS, to perform the operationsdescribed in this application. The hardware components may also access,manipulate, process, create, and store data in response to execution ofthe instructions or software. For simplicity, the singular term“processor” or “computer” may be used in the description of the examplesdescribed in this application, but in other examples multiple processorsor computers may be used, or a processor or computer may includemultiple processing elements, or multiple types of processing elements,or both. For example, a single hardware component or two or morehardware components may be implemented by a single processor, or two ormore processors, or a processor and a controller. One or more hardwarecomponents may be implemented by one or more processors, or a processorand a controller, and one or more other hardware components may beimplemented by one or more other processors, or another processor andanother controller. One or more processors, or a processor and acontroller, may implement a single hardware component, or two or morehardware components. A hardware component may have any one or more ofdifferent processing configurations, examples of which include a singleprocessor, independent processors, parallel processors,single-instruction single-data (SISD) multiprocessing,single-instruction multiple-data (SIMD) multiprocessing,multiple-instruction single-data (MISD) multiprocessing, andmultiple-instruction multiple-data (MIMD) multiprocessing.

The methods illustrated in FIGS. 1-9 that perform the operationsdescribed in this application are performed by computing hardware, forexample, by one or more processors or computers, implemented asdescribed above executing instructions or software to perform theoperations described in this application that are performed by themethods. For example, a single operation or two or more operations maybe performed by a single processor, or two or more processors, or aprocessor and a controller. One or more operations may be performed byone or more processors, or a processor and a controller, and one or moreother operations may be performed by one or more other processors, oranother processor and another controller. One or more processors, or aprocessor and a controller, may perform a single operation, or two ormore operations.

Instructions or software to control computing hardware, for example, oneor more processors or computers, to implement the hardware componentsand perform the methods as described above may be written as computerprograms, code segments, instructions or any combination thereof, forindividually or collectively instructing or configuring the one or moreprocessors or computers to operate as a machine or special-purposecomputer to perform the operations that are performed by the hardwarecomponents and the methods as described above. In one example, theinstructions or software include machine code that is directly executedby the one or more processors or computers, such as machine codeproduced by a compiler. In another example, the instructions or softwareincludes higher-level code that is executed by the one or moreprocessors or computer using an interpreter. The instructions orsoftware may be written using any programming language based on theblock diagrams and the flow charts illustrated in the drawings and thecorresponding descriptions used herein, which disclose algorithms forperforming the operations that are performed by the hardware componentsand the methods as described above.

The instructions or software to control computing hardware, for example,one or more processors or computers, to implement the hardwarecomponents and perform the methods as described above, and anyassociated data, data files, and data structures, may be recorded,stored, or fixed in or on one or more non-transitory computer-readablestorage media. Examples of a non-transitory computer-readable storagemedium include read-only memory (ROM), random-access programmable readonly memory (PROM), electrically erasable programmable read-only memory(EEPROM), random-access memory (RAM), dynamic random access memory(DRAM), static random access memory (SRAM), flash memory, non-volatilememory, CD-ROMs, CD-Rs, CD+Rs, CD-RWs, CD+RWs, DVD-ROMs, DVD-Rs, DVD+Rs,DVD-RWs, DVD+RWs, DVD-RAMs, BD-ROMs, BD-Rs, BD-R LTHs, BD-REs, blue-rayor optical disk storage, hard disk drive (HDD), solid state drive (SSD),flash memory, a card type memory such as multimedia card micro or a card(for example, secure digital (SD) or extreme digital (XD)), magnetictapes, floppy disks, magneto-optical data storage devices, optical datastorage devices, hard disks, solid-state disks, and any other devicethat is configured to store the instructions or software and anyassociated data, data files, and data structures in a non-transitorymanner and provide the instructions or software and any associated data,data files, and data structures to one or more processors or computersso that the one or more processors or computers can execute theinstructions. In one example, the instructions or software and anyassociated data, data files, and data structures are distributed overnetwork-coupled computer systems so that the instructions and softwareand any associated data, data files, and data structures are stored,accessed, and executed in a distributed fashion by the one or moreprocessors or computers.

While this disclosure includes specific examples, it will be apparentafter an understanding of the disclosure of this application thatvarious changes in form and details may be made in these exampleswithout departing from the spirit and scope of the claims and theirequivalents. The examples described herein are to be considered in adescriptive sense only, and not for purposes of limitation. Descriptionsof features or aspects in each example are to be considered as beingapplicable to similar features or aspects in other examples. Suitableresults may be achieved if the described techniques are performed in adifferent order, and/or if components in a described system,architecture, device, or circuit are combined in a different manner,and/or replaced or supplemented by other components or theirequivalents. Therefore, the scope of the disclosure is defined not bythe detailed description, but by the claims and their equivalents, andall variations within the scope of the claims and their equivalents areto be construed as being included in the disclosure.

What is claimed is:
 1. A processor-implemented battery managementmethod, comprising: estimating state information of a plurality ofbattery cells in a battery pack using a first battery state estimationmodel; determining whether state information of at least one of theplurality of battery cells is to be estimated using a second batterystate estimation model based on one or both of whether the stateinformation of the at least one battery cell estimated by the firstmodel is abnormal state information and whether a preset time haselapsed since a previous estimation of battery cell state informationusing the second model; and estimating the state information of the atleast one battery cell using the second model, in response to a resultof the determining being that the state information of the at least onebattery cell is to be estimated using the second model.
 2. The method ofclaim 1, wherein the first model is a lightened model including any oneor any combination of any two or more of an equivalent circuit model, acurrent integration model, and a reduced order model to estimate stateinformation of a battery cell using less computation than the secondmodel.
 3. The method of claim 2, wherein the estimating of the stateinformation of the plurality of battery cells in the battery pack usingthe first model includes determining any one or any combination of anytwo or more of an internal resistance of the equivalent circuit model, acapacitance of the equivalent circuit model, a representative potentialof the reduced order model, and a concentration of the reduced ordermodel.
 4. The method of claim 1, wherein the second model is a precisemodel including the electrochemical model, and wherein the secondbattery state estimation model estimates the state information of the atleast one battery cell by performing a greater amount of computationsthan performed by the first model in estimating the state information ofthe at least one battery cell.
 5. The method of claim 1, wherein thesecond battery state estimation model estimates the state information ofthe at least one battery cell by performing a greater amount ofcomputations than performed by the first model in estimating the stateinformation of the at least one battery cell.
 6. The method of claim 1,wherein the estimating the state information of the at least one batterycell using the second model includes determining either one or both of aconcentration distribution and a potential in an electrode in anelectrochemical model.
 7. The method of claim 1, wherein the determiningof whether the state information of the at least one battery cellestimated by the first model is abnormal state information includescomparing a reference value to either one or both of the stateinformation of the at least one battery cell estimated by the firstmodel, and a value derived from the state information of the at leastone battery cell estimated by the first model.
 8. The method of claim 1,wherein the determining comprises: determining that state information ofthe at least one battery cell is to be estimated using the second modelbased on a predetermined battery cell selection scheme, in response to apreset time elapsing.
 9. The method of claim 8, wherein thepredetermined battery cell selection scheme includes either one or bothof: a round-robin scheme wherein the at least one battery cell isselected from among the plurality of battery cells based on a sequentialorder of the plurality of battery cells; and a random scheme wherein theat least one battery cell is randomly selected from among the pluralityof battery cells.
 10. The method of claim 1, wherein the determiningcomprises determining whether a difference between the state informationof the at least one battery cell estimated by the first model and thestate information of another battery cell of the plurality of batterycells estimated by the first model is greater than or equal to areference value.
 11. The method of claim 1, wherein the determiningcomprises: determining a rate of change over time of the stateinformation of the at least one battery cell estimated by the firstmodel; and determining whether the rate of change is greater than orequal to a reference rate to indicate whether the state information ofthe at least one of the plurality of battery cells is to be estimatedusing the second model.
 12. The method of claim 1, wherein thedetermining comprises determining whether the state information of theat least one battery cell estimated by the first model is within areference range to indicate whether the state information of the atleast one of the plurality of battery cells is to be estimated using thesecond model.
 13. The method of claim 1, wherein the determiningcomprises: determining whether the state information of any one of theplurality of battery cells estimated by the first model is abnormalstate information; determining whether a preset time has elapsed since aprevious estimation of battery cell state information using the secondmodel, in response to determining that none of the state informations ofthe plurality of battery cells are abnormal state information; anddetermining that the state information of the at least one battery cellis to be estimated using the second model in response to either one ofdetermining that the state information of the at least one battery cellestimated by the first model is abnormal state information, anddetermining that the preset time has elapsed.
 14. The method of claim13, wherein the determining comprises determining that the stateinformation of the at least one battery cell is not to be estimatedusing the second model in response to determining that none of the stateinformations of the plurality of battery cells are abnormal stateinformation and the preset time has not elapsed.
 15. The method of claim1, further comprising: verifying whether the state information of the atleast one battery cell estimated using the second model is abnormalstate information; and transmitting, to an external system, informationindicating that the state information of the at least one battery cellis abnormal, in response to verifying that the state information of theat least one battery cell estimated using the second model is abnormalstate information.
 16. The method of claim 1, wherein the stateinformation of the plurality of battery cells includes either one orboth of states of charge (SOCs) and states of health (SOHs) of theplurality of battery cells.
 17. A non-transitory computer-readablestorage medium storing instructions that, when executed by one or moreprocessors, configure the one or more processors to perform the methodof claim
 1. 18. An apparatus, comprising: one or more processorsconfigured to: estimate state information of a plurality of batterycells in a battery pack using a first battery state estimation model,determine whether state information of at least one of the plurality ofbattery cells is to be estimated using a second battery state estimationmodel based on one or both of whether the state information of the atleast one battery cell estimated by the first model is abnormal stateinformation and whether a preset time has elapsed since a previousestimation of battery cell state information using the second mode, andestimate the state information of the at least one battery cell usingthe second model by determining either one or both of a concentrationdistribution and a potential in an electrode in an electrochemicalmodel, in response to determining that the state information of the atleast one battery cell is to be estimated using the second model. 19.The apparatus of claim 18, wherein, for the determining, the one or moreprocessors are configured to determine that state information of the atleast one battery cell is to be estimated using the second model basedon a predetermined battery cell selection scheme, in response to apreset time elapsing.
 20. The apparatus of claim 18, further comprisinga memory storing instructions that, when executed by the one or moreprocessors, configure the one or more processors to perform: theestimating of the state information of the plurality of battery cellsusing the first model, the determining of whether the state informationof the at least one of the plurality of battery cells is to be estimatedusing the second model, and the estimating of the state information ofthe at least one battery cell using the second model.